load dataset - simple contrast (dummy coding) stim Vicarious
mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScA/1stLevel'
mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScA/1stLevel'
con_list = dir(fullfile(mount_dir, '*/con_0025.nii'));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 24360228 bytes
Loading image number: 61
Elapsed time is 223.654575 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5954046 Bit rate: 22.51 bits
check data coverage
m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans') % display
SPM12: spm_check_registration (v7759) 18:37:12 - 26/04/2022
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels: 1 regions
ans = 1×1 cell array
{1×1 region}
drawnow; snapnow
run robfit
set(gcf,'Visible','on')
figure ('Visible', 'on');
out = robfit_parcelwise(con_data_obj);
Loading atlas: CANlab_combined_atlas_object_2018_2mm.mat
Initializing nodes to match regions.
Updating node response data.
Updating obj.connectivity.nodes.
Updating obj.connectivity.nodes.
Updating region averages.
Updating obj.connectivity.regions.
Updating obj.connectivity.regions.
__________________________________________________________________
Input image diagnostic information
__________________________________________________________________
Retained 16 components for mahalanobis distance
Expected 50% of points within 50% normal ellipsoid, found 50.82%
Expected 3.05 outside 95% ellipsoid, found 1
Potential outliers based on mahalanobis distance:
Bonferroni corrected: 0 images Cases
Uncorrected: 1 images Cases 41
Retained 9 components for mahalanobis distance
Expected 50% of points within 50% normal ellipsoid, found 21.31%
Expected 3.05 outside 95% ellipsoid, found 9
Potential outliers based on mahalanobis distance:
Bonferroni corrected: 8 images Cases 10 21 23 28 30 31 48 57
Uncorrected: 9 images Cases 10 15 21 23 28 30 31 48 57
Extracting from gray_matter_mask_sparse.img.
Extracting from canonical_white_matter.img.
Extracting from canonical_ventricles.img.
mean_gray_matter_coverage: 1
global_d_ventricles: 0.5006
global_logp_ventricles: 2.7855
global_d_wm: 0.5754
global_logp_wm: 3.4503
gm_explained_by_csf_pvalue: 5.1841e-07
r2_explained_by_csf: 0.3498
gm_l2norm_explained_by_csf_pvalue: 0
r2_l2norm_explained_by_csf: 0.8583
csf_to_gm_signal_ratio: 1.4624
gm_scale_inhom: 0.7784
csf_scale_inhom: 0.8616
warnings: {1×6 cell}
Warning: Significant global activation in CSF space/ventricles.
- Effect size is d = 0.50
Warning: Significant global activation in white matter.
- Effect size is d = 0.58
Warning: Gray-matter individual diffs significantly correlated with mean CSF value.
- Var explained (r^2) = 34.98%
Warning: Gray-matter scale (L2 norm) significantly correlated with mean CSF L2 norm.
- Var explained (r^2) = 85.83%
Warning: Strong non-zero signal in CSF relative to gray matter.
- Ratio is = 1.46
Warning: High individual diffs in image scaling estimated from CSF.
- CV is = 0.86
Warning: Iteration limit reached.
Warning: Iteration limit reached.
Warning: Iteration limit reached.
Warning: Iteration limit reached.
Warning: Iteration limit reached.
Warning: Iteration limit reached.
Warning: Iteration limit reached.
Warning: Iteration limit reached.
Warning: Iteration limit reached.
Number of unique values in dataset: 1 Bit rate: 0.00 bits
Warning: Number of unique values in dataset is low, indicating possible restriction of bit rate. For comparison, Int16 has 65,536 unique values
Number of unique values in dataset: 1 Bit rate: 0.00 bits
Warning: Number of unique values in dataset is low, indicating possible restriction of bit rate. For comparison, Int16 has 65,536 unique values
Number of unique values in dataset: 1 Bit rate: 0.00 bits
Warning: Number of unique values in dataset is low, indicating possible restriction of bit rate. For comparison, Int16 has 65,536 unique values
__________________________________________________________________
Parcel-wise robust regression
__________________________________________________________________
maxT minP sig05 sig005 sig001 sigFDR05 p_thr_FDR05 min_d_FDR05
______ __________ _____ ______ ______ ________ ___________ ___________
Intercept (Group avg) 4.9099 7.3453e-06 275 67 25 450 0.44687 0.017317
sig*: Significant parcels at given threshold (p < 0.05 two-tailed, q < 0.05 FDR, etc.)
p_thr_FDR05: P-value threshold to achieve q < 0.05 FDR-corrected for each predictor
min_d_FDR05: Min Cohen's d detectable at FDR q < 0.05dashes
__________________________________________________________________
Tables of regions at q < 0.05 FDR
__________________________________________________________________
__________________________________________________________________
Predictor 1: Intercept (Group avg)
__________________________________________________________________
Grouping contiguous voxels: 2 regions
48
____________________________________________________________________________________________________________________________________________
Positive Effects
Region Volume XYZ maxZ modal_label_descriptions Perc_covered_by_label Atlas_regions_covered region_index
____________________ __________ ________________ ______ ___________________________ _____________________ _____________________ ____________
{'Multiple regions'} 1.2897e+06 -4 -26 6 4.9099 {'Cerebellum' } 2 447 1
{'Ctx_IFSp_R' } 8 50 22 26 0.7854 {'Cortex_Fronto_ParietalA'} 100 0 2
Negative Effects
Region Volume XYZ maxZ modal_label_descriptions Perc_covered_by_label Atlas_regions_covered region_index
_____________ ______ ________________ ________ ________________________ _____________________ _____________________ ____________
{'VeP_R' } 544 12 4 -10 -0.82252 {'Basal_ganglia' } 100 1 5
{'Ctx_s32_R'} 824 4 32 -12 -1.3838 {'Cortex_Default_ModeA'} 100 1 4
{'Ctx_13l_R'} 1896 20 24 -20 -0.77366 {'Cortex_Limbic' } 100 1 3
______________________________________________________________
Outlier analysis
______________________________________________________________
global mean | global mean to var | spatial MAD | Missing values | 0 images
Retained 9 components for mahalanobis distance
Expected 50% of points within 50% normal ellipsoid, found 21.31%
Expected 3.05 outside 95% ellipsoid, found 9
Potential outliers based on mahalanobis distance:
Bonferroni corrected: 8 images Cases 10 21 23 28 30 31 48 57
Uncorrected: 9 images Cases 10 15 21 23 28 30 31 48 57
Retained 16 components for mahalanobis distance
Expected 50% of points within 50% normal ellipsoid, found 50.82%
Expected 3.05 outside 95% ellipsoid, found 1
Potential outliers based on mahalanobis distance:
Bonferroni corrected: 0 images Cases
Uncorrected: 1 images Cases 41
Mahalanobis (cov and corr, q<0.05 corrected):
8 images
Outlier_count Percentage
_____________ __________
global_mean 2 3.2787
global_mean_to_variance 1 1.6393
missing_values 0 0
rmssd_dvars 0 0
spatial_variability 1 1.6393
mahal_cov_uncor 9 14.754
mahal_cov_corrected 8 13.115
mahal_corr_uncor 1 1.6393
mahal_corr_corrected 0 0
Overall_uncorrected 10 16.393
Overall_corrected 8 13.115
SPM12: spm_check_registration (v7759) 19:09:06 - 26/04/2022
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
(all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels: 1 regions
Grouping contiguous voxels: 1 regions
Grouping contiguous voxels: 1 regions
